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The application of ensemble Kalman filter in adaptive observation and information content estimation

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Constant with time, and same for 3D-Var ... rawinsonde only ... based on the sensitivity study (impact of the carbon concentration data on the flux estimation) ... – PowerPoint PPT presentation

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Title: The application of ensemble Kalman filter in adaptive observation and information content estimation


1
The application of ensemble Kalman filter in
adaptive observation and information content
estimation studies
  • Junjie Liu and Eugenia Kalnay
  • July 13th, 2007

2
Question to address in adaptive observation study
  • Adaptive observation temporarily adjust
    observation locations
  • Common question how to allocate the limited
    observation resources to maximize effectiveness
    of observations (improve the analysis and
    forecast as much as possible)?
  • Question in hand how to allocate 10 Doppler
    Wind Lidar (DWL) scanning range? (Future DWL will
    operate in adaptive targeting mode (NPOESS P3I
    science team) (observation locations change with
    time)

3
LETKF-based ensemble spread adaptive observation
strategy
  • It is the square root difference between ensemble
    members and ensemble mean state.
  • Ensemble spread estimated from ensemble Kalman
    filter (EnKF) reflects the dynamical
    uncertainties related with background dynamic
    flow..
  • In EnKF the ensemble spread strategy is very
    simple we add the adaptive observations where
    the ensemble spread is large.

4
Rawinsonde observation locations and simulated
satellite winds scanning range
00z and 12z
00z and 12z
06z and 18z
06z and 18z
  • The orbit allows simulated DWL observations
    potentially scanning each location twice a day.
  • Purpose 10 adaptive observations 10 of half
    global grid points.

5
Sampling strategies
  • Ensemble spread strategy (from Local Ensemble
    Transform Kalman Filter)
  • Adaptive observations are at locations with
    large ensemble wind spread at 500hPa.
  • 3D-Var and LETKF have the same adaptive
    observation points
  • Random picking
  • Randomly pick locations from potential
    locations.
  • Uniform distribution
  • Uniformly distributed.
  • Climatology ensemble spread
  • Adaptive observations are at locations with
    large climatological average ensemble wind spread
    from rawinsonde assimilation.
  • Constant with time, and same for 3D-Var and
    LETKF.
  • Ideal sampling
  • Adaptive observations are at locations with
    large background error obtained from the truth.

Change with time
Constant
Impossible in reality
6
500hPa zonal wind RMS error
Rawinsonde climatology uniform random
ensemble spread ideal 100
3D-Var
LETKF
RMSE
  • With 10 adaptive observations, the analysis
    accuracy is significantly improved for both
    3D-Var and LETKF.
  • 3D-Var is more sensitive to adaptive strategies
    than LETKF. Ensemble spread strategy gets best
    result among operational possible strategies

7
500hPa zonal wind RMS error (2 adaptive obs)
Rawinsonde climatology uniform random
ensemble spread ideal 100
3D-Var
LETKF
  • With fewer (2) adaptive observations, ensemble
    spread sampling strategy outperforms the other
    methods in LETKF
  • For 3D-Var, 2 adaptive observations are not
    enough to make significant improvement with any
    method

8
Analysis sensitivity study within LETKF
Analysis mean state
The analysis sensitivity
Degree of Freedom of Signal (DFS) the trace of
the matrix S
  • It can also show the cross sensitivity by
    exploring the off diagonal term.
  • No cost in LETKF assimilation framework.
  • Reflects the observation impact in the analysis.
  • Show the analysis sensitivity to different type
    of observations (rawinsonde, different type of
    satellite observations etc.)

9
Control experiment rawinsonde only
All the dynamical variables (winds, temperature,
specific humidity and surface pressure) are
observed in the observation locations
10
Exp_uv
(winds
are observed in both rawinsonde and dense
network, 30)
Dense wind network
11
Contour RMS error (zonal wind) difference
between rawinsonde and exp_uv
Shaded DFS of zonal
wind in dense network
  • Winds have large impact over the region that does
    not have much rawinsonde, especially over
    Southern Hemisphere and the Tropics.
  • The DFS reflects the wind impact

12
The impact of winds delete-out experiments
  • In both add-on (add winds in the ctrlA
    observation network) and delete-out (delete winds
    in the ctrlB observation network) experiments,
    the winds show the significant impact over
    Tropics
  • Winds have large impact over mid-latitude in SH
    in add-on experiment, but does not shown this
    impact in the delete-out experiment, which is due
    to the significant covariance between winds and
    mass field in this area

13
Possible applications to Carbon problem
  • Observation system design ensemble spread
    method Using the posterior uncertainty
    estimation.
  • Evaluate the significance of the carbon
    observations based on the sensitivity study
    (impact of the carbon concentration data on the
    flux estimation)
  • Will address the essential problem uncertainty
    estimation
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